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Hidden markov and semi-markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series

Hidden markov and semi-markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series
Hidden markov and semi-markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series

We introduce multivariate models for the analysis of stock market returns. Our models are developed under hidden Markov and semi-Markov settings to describe the temporal evolution of returns, whereas the marginal distribution of returns is described by a mixture of multivariate leptokurtic-normal (LN) distributions. Compared to the normal distribution, the LN has an additional parameter governing excess kurtosis and this allows us a better fit to both the distributional and dynamic properties of daily returns. We outline an expectation maximization algorithm for maximum likelihood estimation which exploits recursions developed within the hidden semi-Markov literature. As an illustration, we provide an example based on the analysis of a bivariate time series of stock market returns.

daily returns, elliptical distributions, EM algorithm, hidden Markov model, hidden semi-Markov model, kurtosis, multivariate time series
1479-8409
91-117
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Punzo, Antonio
1138a0c8-cc0b-4f02-8409-957de3bd1fed
Bagnato, Luca
98e5f5a2-2378-4687-ba11-c7351245f127
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Punzo, Antonio
1138a0c8-cc0b-4f02-8409-957de3bd1fed
Bagnato, Luca
98e5f5a2-2378-4687-ba11-c7351245f127

Maruotti, Antonello, Punzo, Antonio and Bagnato, Luca (2018) Hidden markov and semi-markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series. Journal of Financial Econometrics, 17 (1), 91-117. (doi:10.1093/jjfinec/nby019).

Record type: Article

Abstract

We introduce multivariate models for the analysis of stock market returns. Our models are developed under hidden Markov and semi-Markov settings to describe the temporal evolution of returns, whereas the marginal distribution of returns is described by a mixture of multivariate leptokurtic-normal (LN) distributions. Compared to the normal distribution, the LN has an additional parameter governing excess kurtosis and this allows us a better fit to both the distributional and dynamic properties of daily returns. We outline an expectation maximization algorithm for maximum likelihood estimation which exploits recursions developed within the hidden semi-Markov literature. As an illustration, we provide an example based on the analysis of a bivariate time series of stock market returns.

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More information

Accepted/In Press date: 10 July 2018
e-pub ahead of print date: 12 September 2018
Keywords: daily returns, elliptical distributions, EM algorithm, hidden Markov model, hidden semi-Markov model, kurtosis, multivariate time series

Identifiers

Local EPrints ID: 429916
URI: http://eprints.soton.ac.uk/id/eprint/429916
ISSN: 1479-8409
PURE UUID: 0a86ecb1-1ecb-4871-bd57-2e8f6bcac13d

Catalogue record

Date deposited: 09 Apr 2019 16:30
Last modified: 27 Apr 2022 04:05

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Contributors

Author: Antonello Maruotti
Author: Antonio Punzo
Author: Luca Bagnato

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